In reinforcement learning (RL), an agent interacts with an environment and tries to maximize its expected sum of rewards.
Many algorithms, such as Q-learning with \(\epsilon \) -greedy exploration [1]}, can suffer from poor sample complexity [2]}.
This is a problem in real-world situations where an agent may receive a limited amount of samples to learn an optimal policy.
Such real-world environments serve as motivation to reduce the sample complexity of RL algorithms.
